Incentive Design for Crowdsourcing: A Game-Theoretic Approach
published: Oct. 6, 2014, recorded: December 2013, views: 29
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
The Web is increasingly centered around the collective effort of the crowds, with contributions ranging from user-generated content and social media to Citizen Science projects and crowd-based peer-grading and peer-learning in online education. But every crowdsourcing based system relies on users actually participating and making high-quality contributions to function effectively. How can we design systems so that self-interested users — with their own costs and benefits to participation — are properly incentivized to participate and contribute with high effort?
We discuss a game-theoretic framework for incentive design in crowdsourcing, where potential contributors are modeled as self-interested agents who strategically choose whether or not to participate, and how much effort to expend, in response to the incentives offered by the system. We illustrate the game-theoretic approach via the problem of learning the qualities of online content from viewer feedback: here, a learning algorithm needs to not only quickly identify the best contributions, but also simultaneously create incentives for attention-motivated users to make high-quality contributions, leading to a multi-armed bandit problem where the number and success probabilities of the arms of the bandit are endogenously determined in response to the learning algorithm.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !